Novel CineECG enables anatomical 3D localization and classification of bundle branch blocks

AIMS: Ventricular conduction disorders can induce arrhythmias and impair cardiac function. Bundle branch blocks (BBBs) are diagnosed by 12-lead electrocardiogram (ECG), but discrimination between BBBs and normal tracings can be challenging. CineECG computes the temporo-spatial trajectory of activation waveforms in a 3D heart model from 12-lead ECGs. Recently, in Brugada patients, CineECG has localized the terminal components of ventricular depolarization to right ventricle outflow tract (RVOT), coincident with arrhythmogenic substrate localization detected by epicardial electro-anatomical maps. This abnormality was not found in normal or right BBB (RBBB) patients. This study aimed at exploring whether CineECG can improve the discrimination between left BBB (LBBB)/RBBB, and incomplete RBBB (iRBBB). METHODS AND RESULTS: We utilized 500 12-lead ECGs from the online Physionet-XL-PTB-Diagnostic ECG Database with a certified ECG diagnosis. The mean temporo-spatial isochrone trajectory was calculated and projected into the anatomical 3D heart model. We established five CineECG classes: 'Normal', 'iRBBB', 'RBBB', 'LBBB', and 'Undetermined', to which each tracing was allocated. We determined the accuracy of CineECG classification with the gold standard diagnosis. A total of 391 ECGs were analysed (9 ECGs were excluded for noise) and 240/266 were correctly classified as 'normal', 14/17 as 'iRBBB', 55/55 as 'RBBB', 51/51 as 'LBBB', and 31 as 'undetermined'. The terminal mean temporal spatial isochrone contained most information about the BBB localization. CONCLUSION: CineECG provided the anatomical localization of different BBBs and accurately differentiated between normal, LBBB and RBBB, and iRBBB. CineECG may aid clinical diagnostic work-up, potentially contributing to the difficult discrimination between normal, iRBBB, and Brugada patients

Automatic diagnosis of strict left bundle branch block using a wavelet-based approach

Patients with left bundle branch block (LBBB) are known to have a good clinical response to cardiac resynchronization therapy. However, the high number of false positive diagnosis obtained with the conventional LBBB criteria limits the effectiveness of this therapy, which has yielded to the definition of new stricter criteria. They require prolonged QRS duration, a QS or rS pattern in the QRS complexes at leads V1 and V2 and the presence of mid-QRS notch/slurs in 2 leads within V1, V2, V5, V6, I and aVL. The aim of this work was to develop and assess a fully-automatic algorithm for strict LBBB diagnosis based on the wavelet transform. Twelve-lead, high-resolution, 10-second ECGs from 602 patients enrolled in the MADIT-CRT trial were available. Data were labelled for strict LBBB by 2 independent experts and divided into training (n = 300) and validation sets (n = 302) for assessing algorithm performance. After QRS detection, a wavelet-based delineator was used to detect individual QRS waves (Q, R, S), QRS onsets and ends, and to identify the morphological QRS pattern on each standard lead. Then, multilead QRS boundaries were defined in order to compute the global QRS duration. Finally, an automatic algorithm for notch/slur detection within the QRS complex was applied based on the same wavelet approach used for delineation. In the validation set, LBBB was diagnosed with a sensitivity and specificity of Se = 92.9% and Sp = 65.1% (Acc = 79.5%, PPV = 74% and NPV = 89.6%). The results confirmed that diagnosis of strict LBBB can be done based on a fully automatic extraction of temporal and morphological QRS features. However, it became evident that consensus in the definition of QRS duration as well as notch and slurs definitions is necessary in order to guarantee accurate and repeatable diagnosis of complete LBBB

Compelling new electrocardiographic markers for automatic diagnosis

Producción CientíficaBackground and Objective: The automatic diagnosis of heart diseases from the electrocardiogram (ECG) signal is crucial in clinical decision-making. However, the use of computer-based decision rules in clinical practice is still deficient, mainly due to their complexity and a lack of medical interpretation. The objetive of this research is to address these issues by providing valuable diagnostic rules that can be easily implemented in clinical practice. In this research, efficient diagnostic rules friendly in clinical practice are provided. Methods: In this paper, interesting parameters obtained from the ECG signals analysis are presented and two simple rules for automatic diagnosis of Bundle Branch Blocks are defined using new markers derived from the so-called FMM delineator. The main advantages of these markers are the good statistical properties and their clear interpretation in clinically meaningful terms. Results: High sensitivity and specificity values have been obtained using the proposed rules with data from more than 35000 patients from well known benchmarking databases. In particular, to identify Complete Left Bundle Branch Blocks and differentiate this condition from subjects without heart diseases, sensitivity and specificity values ranging from 93% to 99% and from 96% to 99%, respectively. The new markers and the automatic diagnosis are easily available at https://fmmmodel.shinyapps.io/fmmEcg/, an app specifically developed for any given ECG signal. Conclusions: The proposal is different from others in the literature and it is compelling for three main reasons. On the one hand, the markers have a concise electrocardiographic interpretation. On the other hand, the diagnosis rules have a very high accuracy. Finally, the markers can be provided by any device that registers the ECG signal and the automatic diagnosis is made straightforwardly, in contrast to the black-box and deep learning algorithms.Ministerio de Ciencia, Innovación y Universidades (grant PID2019-106363RB-I00